Ejercicio 3¶
In [26]:
%%HTML
<script src="require.js"></script>
In [28]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
from plotly import graph_objects as go
import plotly.io as pio
pio.renderers.default = "notebook_connected"
In [ ]:
datos = pd.read_excel('games_sales_data_PBI.xlsx', sheet_name='games_sales_data_PBI')
#datos.dropna(inplace=True)
datos.head()
print(datos['Year_of_Release'].unique())
[2006. 1985. 2008. 2009. 1996. 1989. 1984. 2005. 1999. 2007. 2010. 2013. 2004. 1990. 1988. 2002. 2001. 2011. 1998. 2015. 2012. 2014. 1992. 1997. 1993. 1994. 1982. 2016. 2003. 1986. 2000. nan 1995. 1991. 1981. 1987. 1980. 1983. 2020. 2017.]
¿Cómo es la distribución porcentual de las ventas para los años 2000, 2005, 2010 y 2015?In [7]:
datos['percentaje_sales'] = (datos['Global_Sales']* 100 / datos['Global_Sales'].sum())
datos_anos = datos[datos['Year_of_Release'].isin([2000, 2005, 2010, 2015])]
porcentajes_por_ano = datos.groupby('Year_of_Release')['percentaje_sales'].sum().reset_index()
porcentajes_anos_seleccionados = datos_anos.groupby('Year_of_Release')['percentaje_sales'].sum().reset_index()
print(porcentajes_anos_seleccionados)
Year_of_Release percentaje_sales 0 2000.0 2.259789 1 2005.0 5.137832 2 2010.0 6.620741 3 2015.0 3.004944
In [8]:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(30, 25))
ax = fig.add_subplot(222)
# Barras para todos los años
bars1 = plt.bar(porcentajes_por_ano['Year_of_Release'], porcentajes_por_ano['percentaje_sales'], label='Todos los años', color='#3682BA')
# Barras para los años seleccionados (2020 y 2005)
bars2 = plt.bar(porcentajes_anos_seleccionados['Year_of_Release'], porcentajes_anos_seleccionados['percentaje_sales'], color='#FA9B58', label='Años 2020 y 2005')
# Agregar valores a las barras
for bar in bars1:
height = bar.get_height()
if height > 0:
plt.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.2f}',
ha='center', va='bottom', fontsize=10, color='black')
for bar in bars2:
height = bar.get_height()
if height > 0:
plt.text(bar.get_x() + bar.get_width() / 2, height, f'{height:.2f}',
ha='center', va='bottom', fontsize=10, color='black')
plt.xlabel('Año')
plt.ylabel('Ventas Globales')
plt.title('Distribución de Ventas Globales por Año')
plt.legend()
plt.show()
In [9]:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(25, 20))
ax = fig.add_subplot(222)
# Barras para todos los años
bars1 = plt.barh(porcentajes_por_ano['Year_of_Release'], porcentajes_por_ano['percentaje_sales'], label='Todos los años', color='#3682BA')
# Barras para los años seleccionados (2020 y 2005)
bars2 = plt.barh(porcentajes_anos_seleccionados['Year_of_Release'], porcentajes_anos_seleccionados['percentaje_sales'], color='#FA9B58', label='Años seleccionados')
# Agregar valores a las barras
for bar in bars1:
width = bar.get_width() # Longitud de la barra en eje X
if width > 0:
plt.text(width + 0.01, bar.get_y() + bar.get_height()/2, f'{width:.2f}',
ha='left', va='center', fontsize=10, color='black')
for bar in bars2:
width = bar.get_width()
if width > 0:
plt.text(width + 0.01, bar.get_y() + bar.get_height()/2, f'{width:.2f}',
ha='left', va='center', fontsize=10, color='black')
plt.ylabel('Año')
plt.xlabel('Porcentaje de Ventas Globales')
plt.title('Distribución del Porcentaje de Ventas Globales por Año')
plt.legend()
plt.show()
¿Qué género aportó más porcentualmente en cada año y por cuánto valor?
In [17]:
datos_generos = datos.groupby('Genre')['Global_Sales'].sum().reset_index()
datos_generos['percentaje_sales'] = (datos_generos['Global_Sales']* 100 / datos_generos['Global_Sales'].sum())
datos_generos = datos_generos.sort_values(by='percentaje_sales', ascending=True)
datos_generos
Out[17]:
| Genre | Global_Sales | percentaje_sales | |
|---|---|---|---|
| 11 | Strategy | 174.50 | 1.956743 |
| 1 | Adventure | 237.69 | 2.665320 |
| 5 | Puzzle | 243.02 | 2.725087 |
| 9 | Simulation | 390.42 | 4.377946 |
| 2 | Fighting | 447.48 | 5.017784 |
| 6 | Racing | 728.90 | 8.173467 |
| 3 | Misc | 803.18 | 9.006401 |
| 4 | Platform | 828.08 | 9.285615 |
| 7 | Role-Playing | 934.40 | 10.477827 |
| 8 | Shooter | 1052.94 | 11.807066 |
| 10 | Sports | 1332.00 | 14.936285 |
| 0 | Action | 1745.27 | 19.570458 |
In [18]:
import matplotlib.pyplot as plt
colors = plt.cm.Spectral(np.linspace(0, 1, len(datos_generos)))
fig = plt.figure(figsize=(25, 20))
ax = fig.add_subplot(222)
# Barras con colores variados
bars1 = plt.barh(datos_generos['Genre'], datos_generos['percentaje_sales'], color=colors, label='Todos los géneros', edgecolor='black')
# Agregar valores a las barras
for bar in bars1:
width = bar.get_width() # Longitud de la barra en eje X
if width > 0:
plt.text(width + 0.05, bar.get_y() + bar.get_height()/2, f'{width:.2f}',
ha='left', va='center', fontsize=10, color='black')
plt.ylabel('Género')
plt.xlabel('Porcentaje de Ventas Globales')
plt.title('Distribución del Porcentaje de Ventas Globales por Género')
plt.show()
Compare 3 géneros en un rango de 4 años entre el 2000 al 2010 e indique ¿dónde presentó cada género mayor auge o ventas?
In [19]:
datos_temporales = datos[['Year_of_Release', 'Global_Sales', 'Genre']]
datos_temporales_wide = datos_temporales.pivot_table(index='Year_of_Release', columns='Genre', values='Global_Sales', aggfunc='sum').fillna(0).reset_index()
#datos_temporales_wide.columns.name = None
datos_temporales_wide
Out[19]:
| Genre | Year_of_Release | Action | Adventure | Fighting | Misc | Platform | Puzzle | Racing | Role-Playing | Shooter | Simulation | Sports | Strategy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980.0 | 0.34 | 0.00 | 0.77 | 2.71 | 0.00 | 0.00 | 0.00 | 0.00 | 7.07 | 0.00 | 0.49 | 0.00 |
| 1 | 1981.0 | 14.84 | 0.00 | 0.00 | 0.00 | 6.93 | 2.24 | 0.48 | 0.00 | 10.04 | 0.45 | 0.79 | 0.00 |
| 2 | 1982.0 | 6.52 | 0.00 | 0.00 | 0.87 | 5.03 | 10.03 | 1.57 | 0.00 | 3.79 | 0.00 | 1.05 | 0.00 |
| 3 | 1983.0 | 2.86 | 0.40 | 0.00 | 2.14 | 6.93 | 0.78 | 0.00 | 0.00 | 0.48 | 0.00 | 3.20 | 0.00 |
| 4 | 1984.0 | 1.85 | 0.00 | 0.00 | 1.45 | 0.69 | 3.14 | 5.95 | 0.00 | 31.10 | 0.00 | 6.18 | 0.00 |
| 5 | 1985.0 | 3.52 | 0.00 | 1.05 | 0.00 | 43.17 | 3.21 | 0.00 | 0.00 | 1.00 | 0.03 | 1.96 | 0.00 |
| 6 | 1986.0 | 13.74 | 0.00 | 0.00 | 0.00 | 9.39 | 0.00 | 1.96 | 2.52 | 3.89 | 0.00 | 5.57 | 0.00 |
| 7 | 1987.0 | 1.12 | 4.38 | 5.42 | 0.00 | 1.74 | 0.00 | 0.00 | 4.65 | 0.71 | 0.00 | 3.72 | 0.00 |
| 8 | 1988.0 | 1.75 | 0.00 | 0.00 | 0.00 | 27.73 | 5.58 | 2.14 | 5.88 | 0.51 | 0.03 | 3.60 | 0.00 |
| 9 | 1989.0 | 4.64 | 0.00 | 0.00 | 1.28 | 20.66 | 37.75 | 0.00 | 2.20 | 1.20 | 0.00 | 5.72 | 0.00 |
| 10 | 1990.0 | 6.39 | 0.00 | 0.00 | 0.00 | 22.97 | 6.00 | 6.26 | 4.52 | 0.00 | 1.14 | 2.11 | 0.00 |
| 11 | 1991.0 | 6.76 | 2.24 | 0.39 | 0.08 | 7.64 | 3.24 | 1.14 | 3.25 | 2.00 | 2.14 | 2.41 | 0.94 |
| 12 | 1992.0 | 3.83 | 12.24 | 15.25 | 4.94 | 13.42 | 4.84 | 9.04 | 6.86 | 0.29 | 2.14 | 2.95 | 0.37 |
| 13 | 1993.0 | 1.81 | 0.07 | 8.75 | 0.30 | 18.67 | 3.17 | 0.36 | 5.59 | 3.08 | 0.19 | 3.18 | 0.81 |
| 14 | 1994.0 | 1.55 | 3.74 | 8.48 | 2.86 | 28.74 | 1.53 | 2.19 | 7.11 | 8.30 | 2.73 | 8.39 | 3.56 |
| 15 | 1995.0 | 3.57 | 0.72 | 14.85 | 6.40 | 16.69 | 2.71 | 6.09 | 14.26 | 4.15 | 4.18 | 7.98 | 6.51 |
| 16 | 1996.0 | 20.58 | 4.19 | 18.06 | 10.68 | 28.23 | 3.91 | 28.24 | 43.96 | 6.91 | 11.33 | 17.45 | 5.61 |
| 17 | 1997.0 | 27.58 | 4.96 | 11.76 | 5.69 | 21.85 | 5.89 | 31.91 | 21.79 | 22.17 | 9.65 | 30.02 | 7.71 |
| 18 | 1998.0 | 39.44 | 9.05 | 31.66 | 11.94 | 29.88 | 6.34 | 27.90 | 28.08 | 9.80 | 7.11 | 41.79 | 13.46 |
| 19 | 1999.0 | 27.77 | 7.60 | 14.26 | 20.65 | 20.82 | 1.36 | 37.27 | 49.09 | 12.25 | 11.44 | 30.29 | 18.45 |
| 20 | 2000.0 | 34.04 | 2.98 | 20.22 | 15.54 | 16.06 | 3.82 | 19.99 | 29.03 | 6.81 | 3.38 | 41.19 | 8.52 |
| 21 | 2001.0 | 59.39 | 9.12 | 18.12 | 16.40 | 39.28 | 8.00 | 55.81 | 22.06 | 24.77 | 19.54 | 51.43 | 7.55 |
| 22 | 2002.0 | 86.76 | 11.05 | 25.02 | 15.67 | 45.97 | 5.34 | 30.20 | 45.13 | 48.58 | 10.81 | 65.42 | 5.56 |
| 23 | 2003.0 | 67.93 | 2.14 | 23.73 | 23.82 | 42.89 | 2.42 | 52.19 | 30.26 | 27.13 | 21.19 | 56.11 | 7.99 |
| 24 | 2004.0 | 76.25 | 8.69 | 16.78 | 31.32 | 47.22 | 8.40 | 47.83 | 53.95 | 46.95 | 10.83 | 63.67 | 7.16 |
| 25 | 2005.0 | 85.53 | 8.44 | 19.71 | 61.08 | 23.39 | 20.42 | 56.03 | 28.42 | 41.55 | 48.92 | 59.51 | 5.31 |
| 26 | 2006.0 | 66.13 | 11.37 | 22.42 | 67.07 | 49.35 | 10.80 | 33.86 | 57.37 | 38.24 | 21.56 | 135.83 | 4.22 |
| 27 | 2007.0 | 104.97 | 24.10 | 17.49 | 91.52 | 35.10 | 23.74 | 38.87 | 43.45 | 70.67 | 48.42 | 97.78 | 9.26 |
| 28 | 2008.0 | 135.01 | 24.71 | 35.04 | 86.05 | 35.14 | 15.29 | 70.03 | 59.03 | 59.22 | 46.09 | 94.63 | 11.55 |
| 29 | 2009.0 | 137.66 | 20.20 | 31.88 | 75.80 | 40.58 | 19.80 | 33.65 | 47.29 | 69.30 | 33.03 | 137.33 | 12.36 |
| 30 | 2010.0 | 115.49 | 16.00 | 14.72 | 95.20 | 30.98 | 10.74 | 34.36 | 69.57 | 76.87 | 21.55 | 91.34 | 13.77 |
| 31 | 2011.0 | 116.76 | 15.61 | 22.37 | 54.45 | 27.71 | 4.95 | 34.83 | 52.85 | 98.17 | 15.13 | 56.12 | 8.84 |
| 32 | 2012.0 | 119.10 | 5.80 | 9.20 | 22.21 | 18.37 | 1.72 | 13.68 | 46.91 | 71.80 | 13.36 | 30.42 | 3.27 |
| 33 | 2013.0 | 122.57 | 6.07 | 7.04 | 25.28 | 24.59 | 0.95 | 12.33 | 44.42 | 62.05 | 8.62 | 41.23 | 6.09 |
| 34 | 2014.0 | 97.30 | 5.61 | 15.83 | 23.35 | 8.78 | 1.49 | 16.64 | 45.57 | 65.26 | 5.59 | 45.10 | 0.99 |
| 35 | 2015.0 | 72.04 | 8.15 | 7.92 | 11.56 | 6.06 | 0.72 | 8.07 | 37.66 | 67.49 | 5.64 | 40.90 | 1.84 |
| 36 | 2016.0 | 30.23 | 3.83 | 4.44 | 2.63 | 3.21 | 0.01 | 2.83 | 18.12 | 38.23 | 1.89 | 23.53 | 1.15 |
| 37 | 2017.0 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | 0.00 |
| 38 | 2020.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.29 | 0.00 | 0.00 |
In [21]:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(20, 10))
# Trazar las líneas correctamente usando plt.plot()
plt.plot(datos_temporales_wide['Year_of_Release'], datos_temporales_wide['Action'], label='Action', color='#9E0142', linestyle='--')
plt.plot(datos_temporales_wide['Year_of_Release'], datos_temporales_wide['Shooter'], label='Shooter', color='#FA9B58', linestyle='-.')
plt.plot(datos_temporales_wide['Year_of_Release'], datos_temporales_wide['Sports'], label='Sports', color='#3682BA', linestyle=':')
# Años a resaltar
years_to_highlight = [2006, 2007, 2008, 2009]
# Filtrar los datos para esos años
highlight_data = datos_temporales_wide[datos_temporales_wide['Year_of_Release'].isin(years_to_highlight)]
# Agregar puntos destacados con plt.scatter()
plt.scatter(highlight_data['Year_of_Release'], highlight_data['Action'], color='#9E0142', s=100, label='Action (Resaltado)')
plt.scatter(highlight_data['Year_of_Release'], highlight_data['Shooter'], color='#FA9B58', s=100, label='Shooter (Resaltado)')
plt.scatter(highlight_data['Year_of_Release'], highlight_data['Sports'], color='#3682BA', s=100, label='Sports (Resaltado)')
plt.xlabel('Año')
plt.ylabel('Ventas Globales')
plt.title('Ventas Globales por Género en el Tiempo')
plt.legend()
plt.show()
Ejercicio 4¶
Se debe escoger un género de videojuegos. Para el género escogido se debe responder:
¿Cómo es la distribución percentil? ¿dónde presenta mayor agrupamiento de los datos?In [22]:
Action = datos[datos.Genre == 'Action']
Action
Out[22]:
| Name | Platform | Year_of_Release | Genre | Publisher | NA_Sales | EU_Sales | JP_Sales | Other_Sales | Global_Sales | Critic_Score | Critic_Count | User_Score | User_Count | Developer | Rating | percentaje_sales | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16 | Grand Theft Auto V | PS3 | 2013.0 | Action | Take-Two Interactive | 7.02 | 9.09 | 0.98 | 3.96 | 21.04 | 97.0 | 50.0 | 8.2 | 3994.0 | Rockstar North | M | 0.235867 |
| 17 | Grand Theft Auto: San Andreas | PS2 | 2004.0 | Action | Take-Two Interactive | 9.43 | 0.40 | 0.41 | 10.57 | 20.81 | 95.0 | 80.0 | 9 | 1588.0 | Rockstar North | M | 0.233288 |
| 23 | Grand Theft Auto V | X360 | 2013.0 | Action | Take-Two Interactive | 9.66 | 5.14 | 0.06 | 1.41 | 16.27 | 97.0 | 58.0 | 8.1 | 3711.0 | Rockstar North | M | 0.182393 |
| 24 | Grand Theft Auto: Vice City | PS2 | 2002.0 | Action | Take-Two Interactive | 8.41 | 5.49 | 0.47 | 1.78 | 16.15 | 95.0 | 62.0 | 8.7 | 730.0 | Rockstar North | M | 0.181048 |
| 38 | Grand Theft Auto III | PS2 | 2001.0 | Action | Take-Two Interactive | 6.99 | 4.51 | 0.30 | 1.30 | 13.10 | 97.0 | 56.0 | 8.5 | 664.0 | DMA Design | M | 0.146856 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 16696 | Metal Gear Solid V: Ground Zeroes | PC | 2014.0 | Action | Konami Digital Entertainment | 0.00 | 0.01 | 0.00 | 0.00 | 0.01 | 80.0 | 20.0 | 7.6 | 412.0 | Kojima Productions | M | 0.000112 |
| 16698 | Carmageddon 64 | N64 | 1999.0 | Action | Virgin Interactive | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | NaN | NaN | NaN | NaN | NaN | NaN | 0.000112 |
| 16699 | Planet Monsters | GBA | 2001.0 | Action | Titus | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 67.0 | 9.0 | tbd | NaN | Planet Interactive | E | 0.000112 |
| 16703 | The Longest 5 Minutes | PSV | 2016.0 | Action | Nippon Ichi Software | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | NaN | NaN | NaN | NaN | NaN | NaN | 0.000112 |
| 16714 | Samurai Warriors: Sanada Maru | PS3 | 2016.0 | Action | Tecmo Koei | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | NaN | NaN | NaN | NaN | NaN | NaN | 0.000112 |
3370 rows × 17 columns
In [23]:
fig = plt.figure(figsize=(12, 6))
fig1 = fig.add_subplot(2,1,1)
fig1.boxplot([Action['Global_Sales']], labels=['Action'],
patch_artist=True,
boxprops=dict(facecolor='#9E0142', color='white'),
vert=False,
showfliers=True)
fig1.set_ylabel('Género Acción')
#fig1.set_xlabel('Ventas Globales (en millones)')
#fig1.set_title('Ventas Globales del Género Action')
fig2 = fig.add_subplot(2,1,2)
fig2.boxplot([Action['Global_Sales']], labels=['Action'],
patch_artist=True,
boxprops=dict(facecolor='#9E0142', color='white'),
vert=False,
showfliers=False)
fig2.set_ylabel('Género Acción')
fig2.set_xlabel('Ventas Globales (en millones)')
#fig2.set_title('Ventas Globales del Género Action')
plt.tight_layout() # Ajustar el espacio entre los gráficos
plt.show()
/tmp/ipykernel_3694/244920339.py:5: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11. fig1.boxplot([Action['Global_Sales']], labels=['Action'], /tmp/ipykernel_3694/244920339.py:17: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11. fig2.boxplot([Action['Global_Sales']], labels=['Action'],
En una vista superpuesta indique o compare los scores para el género escogido. ¿Hay cercanía entre las valoraciones de los usuarios y la de los críticos?
In [25]:
import plotly.graph_objects as go
import plotly.subplots as sp
import seaborn as sns
import ipywidgets as widgets
from IPython.display import display, clear_output
# Asegurar que 'Year_of_Release' sea de tipo entero y eliminar valores NaN
df_regiones = datos.dropna(subset=['Year_of_Release', 'NA_Sales', 'EU_Sales', 'JP_Sales']).copy()
df_regiones['Year_of_Release'] = df_regiones['Year_of_Release'].astype(int)
df_generos = datos.dropna(subset=['Year_of_Release', 'Genre', 'Global_Sales']).copy()
df_generos['Year_of_Release'] = df_generos['Year_of_Release'].astype(int)
# Filtrar solo los géneros de interés
generos_interes = ["Action", "Role-Playing", "Strategy", "Misc"]
df_generos = df_generos[df_generos["Genre"].isin(generos_interes)]
# Agrupar ventas por año
ventas_por_region = df_regiones.groupby("Year_of_Release")[["NA_Sales", "EU_Sales", "JP_Sales"]].sum().reset_index()
ventas_por_genero = df_generos.groupby(["Year_of_Release", "Genre"])["Global_Sales"].sum().reset_index()
# Widget para seleccionar el rango de años
year_range = widgets.IntRangeSlider(
value=[ventas_por_region["Year_of_Release"].min(), ventas_por_region["Year_of_Release"].max()],
min=ventas_por_region["Year_of_Release"].min(),
max=ventas_por_region["Year_of_Release"].max(),
step=1,
description='Años:',
continuous_update=False
)
# Función para actualizar ambas gráficas
def actualizar_graficos(year_range):
start_year, end_year = year_range # Obtener el rango de años seleccionado
df_region_filtrado = ventas_por_region[(ventas_por_region["Year_of_Release"] >= start_year) &
(ventas_por_region["Year_of_Release"] <= end_year)]
df_genero_filtrado = ventas_por_genero[(ventas_por_genero["Year_of_Release"] >= start_year) &
(ventas_por_genero["Year_of_Release"] <= end_year)]
# Limpiar salida y mostrar el selector de rango
clear_output(wait=True)
# Definir colores de la paleta Spectral
colores = sns.color_palette("Spectral", 7).as_hex()
colores_regiones = {"NA_Sales": colores[0], "EU_Sales": colores[1], "JP_Sales": colores[2]}
colores_generos = {"Action": colores[3], "Role-Playing": colores[4],
"Strategy": colores[5], "Misc": colores[6]}
# Crear subplot con 2 filas y 1 columna
fig = sp.make_subplots(rows=2, cols=1,
subplot_titles=("Tendencia de Ventas por Región",
"Tendencia de Ventas por Género"))
# Gráfica 1: Tendencia de ventas por región
for region in ["NA_Sales", "EU_Sales", "JP_Sales"]:
fig.add_trace(go.Scatter(
x=df_region_filtrado["Year_of_Release"],
y=df_region_filtrado[region],
mode='lines+markers',
name={"NA_Sales": "EE.UU", "EU_Sales": "Europa", "JP_Sales": "Japón"}[region],
line=dict(color=colores_regiones[region], width=2),
marker=dict(size=6),
hovertemplate=f"<b>{region}</b><br>Año: %{{x}}<br>Ventas: %{{y:.2f}}M USD<extra></extra>"
), row=1, col=1)
# Gráfica 2: Tendencia de ventas por género
for genero in generos_interes:
df_genero = df_genero_filtrado[df_genero_filtrado["Genre"] == genero]
fig.add_trace(go.Scatter(
x=df_genero["Year_of_Release"],
y=df_genero["Global_Sales"],
mode='lines+markers',
name=genero,
line=dict(color=colores_generos[genero], width=2),
marker=dict(size=6),
hovertemplate=f"<b>{genero}</b><br>Año: %{{x}}<br>Ventas: %{{y:.2f}}M USD<extra></extra>"
), row=2, col=1)
# Personalizar diseño de la gráfica
fig.update_layout(
title=dict(
text="Tendencia de Ventas en Regiones y Géneros",
font=dict(family="serif", size=22, color="black", weight="bold"), # Fuente y tamaño del título
x=0.5 # Centrar el título
),
plot_bgcolor='white',
height=800,
legend=dict(
title_text="Categoría",
font=dict(family="serif", size=14, color="black", weight="normal") # Fuente y tamaño de la leyenda
)
)
# Ajustes individuales para cada eje X e Y con fuentes personalizadas
fig.update_xaxes(title="Año", tickfont=dict(family="serif", size=16, color="black", weight="bold"), row=1, col=1)
fig.update_xaxes(title="Año", tickfont=dict(family="serif", size=16, color="black", weight="bold"), row=2, col=1)
fig.update_yaxes(title="Ventas (Millones USD)", tickfont=dict(family="serif", size=16, color="black", weight="bold"), row=1, col=1)
fig.update_yaxes(title="Ventas (Millones USD)", tickfont=dict(family="serif", size=16, color="black", weight="bold"), row=2, col=1)
# Mostrar la gráfica
fig.show()
# Mostrar el selector de años y la gráfica inicial
display(year_range)
actualizar_graficos(year_range.value)
# Conectar el widget al cambio de selección
year_range.observe(lambda change: actualizar_graficos(change["new"]), names='value')
¿Cuál es el valor del score aproximadamente donde un videojuego alcanzó el mayor número de ventas?
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¿Cuál es la distribución de la correlación para el género escogido?
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¿Qué relación existe entre los scores de los jugadores y de la crítica para el género escogido?
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